
import Head from 'next/head'

<Head>
  <script>
    {
      `(function() {
         var _hmt = _hmt || [];
(function() {
  var hm = document.createElement("script");
  hm.src = "https://hm.baidu.com/hm.js?e60fb290e204e04c5cb6f79b0ac1e697";
  var s = document.getElementsByTagName("script")[0]; 
  s.parentNode.insertBefore(hm, s);
})();
       })();`
    }
  </script>
</Head>

![LangChain](https://pica.zhimg.com/50/v2-56e8bbb52aa271012541c1fe1ceb11a2_r.gif)






 Images
 [#](#images "Permalink to this headline")
===================================================

这包括如何将诸如 JPG 或 PNG 之类的图像加载到我们可以在下游使用的文档格式中。





使用非结构化 Using Unstructured
 [#](#using-unstructured "Permalink to this headline")
---------------------------------------------------------------------------







```python
from langchain.document_loaders.image import UnstructuredImageLoader

```










```python
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg")

```










```python
data = loader.load()

```










```python
data[0]

```








```python
Document(page_content="LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\n\n\n‘Zxjiang Shen' (F3}, Ruochen Zhang”, Melissa Dell*, Benjamin Charles Germain\nLeet, Jacob Carlson, and Weining LiF\n\n\nsugehen\n\nshangthrows, et\n\n“Abstract. Recent advanocs in document image analysis (DIA) have been\n‘pimarliy driven bythe application of neural networks dell roar\n{uteomer could be aly deployed in production and extended fo farther\n[nvetigtion. However, various factory ke lcely organize codebanee\nsnd sophisticated modal cnigurations compat the ey ree of\n‘erin! innovation by wide sence, Though there have been sng\n‘Hors to improve reuablty and simplify deep lees (DL) mode\n‘aon, sone of them ae optimized for challenge inthe demain of DIA,\nThis roprscte a major gap in the extng fol, sw DIA i eal to\nscademic research acon wie range of dpi in the social ssencee\n[rary for streamlining the sage of DL in DIA research and appicn\n‘tons The core LayoutFaraer brary comes with a sch of simple and\nIntative interfaee or applying and eutomiing DI. odel fr Inyo de\npltfom for sharing both protrined modes an fal document dist\n{ation pipeline We demonutate that LayootPareer shea fr both\nlightweight and lrgeseledgtieation pipelines in eal-word uae ces\nThe leary pblely smal at Btspe://layost-pareergsthab So\n\n\n\n‘Keywords: Document Image Analysis» Deep Learning Layout Analysis\n‘Character Renguition - Open Serres dary « Tol\n\n\nIntroduction\n\n\n‘Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndoctiment image analysis (DIA) tea including document image clasiffeation [I]\n", lookup_str='', metadata={'source': 'layout-parser-paper-fast.jpg'}, lookup_index=0)

```






### Retain Elements [#](#retain-elements "Permalink to this headline")



在幕后，非结构化为不同的文本块创建不同的“元素”。

默认情况下，我们将它们组合在一起，但您可以通过指定`mode="elements"`轻松保持这种分离。







```python
loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg", mode="elements")

```










```python
data = loader.load()

```










```python
data[0]

```








```python
Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\n', lookup_str='', metadata={'source': 'layout-parser-paper-fast.jpg', 'filename': 'layout-parser-paper-fast.jpg', 'page_number': 1, 'category': 'Title'}, lookup_index=0)

```









